Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN
The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a...
Saved in:
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/13/6/75 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850050906583728128 |
|---|---|
| author | Minji Kang Sung Kyu Jang Jihun Kim Seongho Kim Changmin Kim Hyo-Chang Lee Wooseok Kang Min Sup Choi Hyeongkeun Kim Hyeong-U Kim |
| author_facet | Minji Kang Sung Kyu Jang Jihun Kim Seongho Kim Changmin Kim Hyo-Chang Lee Wooseok Kang Min Sup Choi Hyeongkeun Kim Hyeong-U Kim |
| author_sort | Minji Kang |
| collection | DOAJ |
| description | The precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF<sub>4</sub>-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments. |
| format | Article |
| id | doaj-art-5a6da56cd858405ab2d3f9c27a5ee8db |
| institution | DOAJ |
| issn | 2224-2708 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj-art-5a6da56cd858405ab2d3f9c27a5ee8db2025-08-20T02:53:19ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082024-11-011367510.3390/jsan13060075Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGANMinji Kang0Sung Kyu Jang1Jihun Kim2Seongho Kim3Changmin Kim4Hyo-Chang Lee5Wooseok Kang6Min Sup Choi7Hyeongkeun Kim8Hyeong-U Kim9Semiconductor Manufacturing Research Center, Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Republic of KoreaElectronic Convergence Material and Device Research Center, Korea Electronics Technology Institute (KETI), Seongnam 13509, Republic of KoreaElectronic Convergence Material and Device Research Center, Korea Electronics Technology Institute (KETI), Seongnam 13509, Republic of KoreaSemiconductor Manufacturing Research Center, Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Republic of KoreaMemory Etch Technology Team, Samsung Electronics, Pyeongtaek 17786, Republic of KoreaSchool of Electronics and Computer Engineering, Korea Aerospace University (KAU), Goyang 10540, Republic of KoreaSemiconductor Manufacturing Research Center, Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Republic of KoreaDepartment of Materials Science and Engineering, Chungnam National University (CNU), Daejeon 34134, Republic of KoreaElectronic Convergence Material and Device Research Center, Korea Electronics Technology Institute (KETI), Seongnam 13509, Republic of KoreaSemiconductor Manufacturing Research Center, Korea Institute of Machinery and Materials (KIMM), Daejeon 34103, Republic of KoreaThe precise monitoring of chemical reactions in plasma-based processes is crucial for advanced semiconductor manufacturing. This study integrates three diagnostic techniques—Optical Emission Spectroscopy (OES), Quadrupole Mass Spectrometry (QMS), and Time-of-Flight Mass Spectrometry (ToF-MS)—into a reactive ion etcher (RIE) system to analyze CF<sub>4</sub>-based plasma. To synchronize and integrate data from these different domains, we developed a Tri-CycleGAN model that utilizes three interconnected CycleGANs for bi-directional data transformation between OES, QMS, and ToF-MS. This configuration enables accurate mapping of data across domains, effectively compensating for the blind spots of individual diagnostic techniques. The model incorporates self-attention mechanisms to address temporal misalignments and a direct loss function to preserve fine-grained features, further enhancing data accuracy. Experimental results show that the Tri-CycleGAN model achieves high consistency in reconstructing plasma measurement data under various conditions. The model’s ability to fuse multi-domain diagnostic data offers a robust solution for plasma monitoring, potentially improving precision, yield, and process control in semiconductor manufacturing. This work lays a foundation for future applications of machine learning-based diagnostic integration in complex plasma environments.https://www.mdpi.com/2224-2708/13/6/75plasma diagnosticsreactive ion etching (RIE)semiconductor manufacturingmachine learningcycle-consistent adversarial network (CycleGAN)multi-domain data integration |
| spellingShingle | Minji Kang Sung Kyu Jang Jihun Kim Seongho Kim Changmin Kim Hyo-Chang Lee Wooseok Kang Min Sup Choi Hyeongkeun Kim Hyeong-U Kim Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN Journal of Sensor and Actuator Networks plasma diagnostics reactive ion etching (RIE) semiconductor manufacturing machine learning cycle-consistent adversarial network (CycleGAN) multi-domain data integration |
| title | Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN |
| title_full | Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN |
| title_fullStr | Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN |
| title_full_unstemmed | Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN |
| title_short | Multi-Domain Data Integration for Plasma Diagnostics in Semiconductor Manufacturing Using Tri-CycleGAN |
| title_sort | multi domain data integration for plasma diagnostics in semiconductor manufacturing using tri cyclegan |
| topic | plasma diagnostics reactive ion etching (RIE) semiconductor manufacturing machine learning cycle-consistent adversarial network (CycleGAN) multi-domain data integration |
| url | https://www.mdpi.com/2224-2708/13/6/75 |
| work_keys_str_mv | AT minjikang multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT sungkyujang multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT jihunkim multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT seonghokim multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT changminkim multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT hyochanglee multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT wooseokkang multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT minsupchoi multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT hyeongkeunkim multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan AT hyeongukim multidomaindataintegrationforplasmadiagnosticsinsemiconductormanufacturingusingtricyclegan |